






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[str]=13       ,				lowerCamelCase		:			Optional[int]=7       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Optional[Any]=True       ,				lowerCamelCase		:			Dict=99       ,				lowerCamelCase		:			Optional[int]=32       ,				lowerCamelCase		:			Optional[Any]=5       ,				lowerCamelCase		:			Optional[int]=4       ,				lowerCamelCase		:			List[Any]=37       ,				lowerCamelCase		:			Dict="gelu"       ,				lowerCamelCase		:			Union[str, Any]=0.1       ,				lowerCamelCase		:			Optional[int]=0.1       ,				lowerCamelCase		:			List[Any]=512       ,				lowerCamelCase		:			List[Any]=16       ,				lowerCamelCase		:			Optional[int]=2       ,				lowerCamelCase		:			Tuple=0.02       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			int=4       ,				lowerCamelCase		:			List[str]=None       ,				)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    seq_length
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_input_mask
         _UpperCAmelCase							=    use_token_type_ids
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    vocab_size
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    max_position_embeddings
         _UpperCAmelCase							=    type_vocab_size
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    num_choices
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			Tuple							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size							)

         _UpperCAmelCase							=    None
         if self.use_input_mask:
                _UpperCAmelCase							=    random_attention_mask([self.batch_size, self.seq_length]							)

         _UpperCAmelCase							=    None
         if self.use_token_type_ids:
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size							)

         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels							)
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.num_choices							)

         _UpperCAmelCase							=    self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         return LlamaConfig(
             vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    LlamaModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)
         _UpperCAmelCase							=    model(lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			int       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    LlamaModel(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				)
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				)
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			int       ,				)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    LlamaForCausalLM(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)							)


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			int       ,				lowerCamelCase		:			List[Any]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    LlamaForCausalLM(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()

         # first forward pass
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				use_cache=lowerCamelCase       ,				)
         _UpperCAmelCase							=    outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         _UpperCAmelCase							=    ids_tensor((self.batch_size, 3)       ,				config.vocab_size							)
         _UpperCAmelCase							=    ids_tensor((self.batch_size, 3)       ,				vocab_size=2							)

         # append to next input_ids and
         _UpperCAmelCase							=    torch.cat([input_ids, next_tokens]       ,				dim=-1							)
         _UpperCAmelCase							=    torch.cat([input_mask, next_mask]       ,				dim=-1							)

         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				output_hidden_states=lowerCamelCase       ,				)["""hidden_states"""][0]
         _UpperCAmelCase							=    model(
             lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				encoder_hidden_states=lowerCamelCase       ,				encoder_attention_mask=lowerCamelCase       ,				past_key_values=lowerCamelCase       ,				output_hidden_states=lowerCamelCase       ,				)["""hidden_states"""][0]

         # select random slice
         _UpperCAmelCase							=    ids_tensor((1,)       ,				output_from_past.shape[-1]							).item()
         _UpperCAmelCase							=    output_from_no_past[:, -3:, random_slice_idx].detach()
         _UpperCAmelCase							=    output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-3							)							)





  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			(
             _UpperCAmelCase
         )	,			
         )							=    config_and_inputs
         _UpperCAmelCase							=    {"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
  _lowerCamelCase    =  (LlamaForCausalLM,) if is_torch_available() else ()
  _lowerCamelCase    =  (
      {
          '''feature-extraction''': LlamaModel,
          '''text-classification''': LlamaForSequenceClassification,
          '''text-generation''': LlamaForCausalLM,
          '''zero-shot''': LlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    LlamaModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         self.config_tester.run_common_tests()


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
                _UpperCAmelCase							=    type
                self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    input_dict["""input_ids"""]
         _UpperCAmelCase							=    input_ids.ne(1							).to(lowerCamelCase							)
         _UpperCAmelCase							=    ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size							)
         _UpperCAmelCase							=    LlamaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


  def  lowerCamelCase       (      self		:			int							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    """single_label_classification"""
         _UpperCAmelCase							=    input_dict["""input_ids"""]
         _UpperCAmelCase							=    input_ids.ne(1							).to(lowerCamelCase							)
         _UpperCAmelCase							=    ids_tensor([self.model_tester.batch_size]       ,				self.model_tester.type_sequence_label_size							)
         _UpperCAmelCase							=    LlamaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    3
         _UpperCAmelCase							=    """multi_label_classification"""
         _UpperCAmelCase							=    input_dict["""input_ids"""]
         _UpperCAmelCase							=    input_ids.ne(1							).to(lowerCamelCase							)
         _UpperCAmelCase							=    ids_tensor(
             [self.model_tester.batch_size, config.num_labels]       ,				self.model_tester.type_sequence_label_size							).to(torch.float							)
         _UpperCAmelCase							=    LlamaForSequenceClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				attention_mask=lowerCamelCase       ,				labels=lowerCamelCase							)
         self.assertEqual(result.logits.shape       ,				(self.model_tester.batch_size, self.model_tester.num_labels)							)


  @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""							)
  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         pass





  @parameterized.expand([("""linear""",), ("""dynamic""",)]							)
  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase							=    ids_tensor([1, 10]       ,				config.vocab_size							)
         _UpperCAmelCase							=    ids_tensor([1, int(config.max_position_embeddings * 1.5							)]       ,				config.vocab_size							)

         set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
         _UpperCAmelCase							=    LlamaModel(lowerCamelCase							)
         original_model.to(lowerCamelCase							)
         original_model.eval()
         _UpperCAmelCase							=    original_model(lowerCamelCase							).last_hidden_state
         _UpperCAmelCase							=    original_model(lowerCamelCase							).last_hidden_state

         set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
         _UpperCAmelCase							=    {"""type""": scaling_type, """factor""": 10.0}
         _UpperCAmelCase							=    LlamaModel(lowerCamelCase							)
         scaled_model.to(lowerCamelCase							)
         scaled_model.eval()
         _UpperCAmelCase							=    scaled_model(lowerCamelCase							).last_hidden_state
         _UpperCAmelCase							=    scaled_model(lowerCamelCase							).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
                self.assertTrue(torch.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-5							)							)
         else:
                self.assertFalse(torch.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-5							)							)

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(lowerCamelCase       ,				lowerCamelCase       ,				atol=1E-5							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""							)
  @slow
  def  lowerCamelCase       (      self		:			Tuple							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor([input_ids]							)							)
         # Expected mean on dim = -1
         _UpperCAmelCase							=    torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]							)
         torch.testing.assert_close(out.mean(-1							)       ,				lowerCamelCase       ,				atol=1E-2       ,				rtol=1E-2							)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         _UpperCAmelCase							=    torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]							)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]       ,				lowerCamelCase       ,				atol=1E-5       ,				rtol=1E-5							)


  @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""							)
  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor(lowerCamelCase							)							)
         # Expected mean on dim = -1
         _UpperCAmelCase							=    torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]							)
         torch.testing.assert_close(out.mean(-1							)       ,				lowerCamelCase       ,				atol=1E-2       ,				rtol=1E-2							)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         _UpperCAmelCase							=    torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]							)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]       ,				lowerCamelCase       ,				atol=1E-5       ,				rtol=1E-5							)


  @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""							)
  @slow
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor(lowerCamelCase							)							)
         # Expected mean on dim = -1
         _UpperCAmelCase							=    torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]							)
         torch.testing.assert_close(out.mean(-1							)       ,				lowerCamelCase       ,				atol=1E-2       ,				rtol=1E-2							)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         _UpperCAmelCase							=    torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]							)
         # fmt: on
         torch.testing.assert_close(out.mean(-1							)       ,				lowerCamelCase       ,				atol=1E-2       ,				rtol=1E-2							)


  @unittest.skip(
      """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""							)
  @slow
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    [1, 306, 4658, 278, 6593, 310, 2834, 338]
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""       ,				device_map="""auto"""							)
         _UpperCAmelCase							=    model(torch.tensor(lowerCamelCase							)							)

         _UpperCAmelCase							=    torch.tensor(
             [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]       ,				dtype=torch.floataa							)
         torch.testing.assert_close(out.mean(-1							)       ,				lowerCamelCase       ,				atol=1E-2       ,				rtol=1E-2							)
         # fmt: off
         _UpperCAmelCase							=    torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]							)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]       ,				lowerCamelCase       ,				atol=1E-5       ,				rtol=1E-5							)





  @unittest.skip("""Model is curently gated"""							)
  @slow
  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
         _UpperCAmelCase							=    """Simply put, the theory of relativity states that """
         _UpperCAmelCase							=    LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""							)
         _UpperCAmelCase							=    tokenizer.encode(lowerCamelCase       ,				return_tensors="""pt"""							)
         _UpperCAmelCase							=    LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf"""       ,				device_map="""sequential"""       ,				use_safetensors=lowerCamelCase							)

         # greedy generation outputs
         _UpperCAmelCase							=    model.generate(lowerCamelCase       ,				max_new_tokens=64       ,				top_p=lowerCamelCase       ,				temperature=1       ,				do_sample=lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.decode(generated_ids[0]       ,				skip_special_tokens=lowerCamelCase							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



